Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants

Mood disorders, including depression and anxiety, affect almost one-fifth of the world’s adult population and are becoming increasingly prevalent. Mutations in circadian clock genes have previously been associated with mood disorders both directly and indirectly through alterations in circadian phase, suggesting that the circadian clock influences multiple molecular pathways involved in mood. By targeting previously identified single nucleotide polymorphisms (SNPs) that have been implicated in anxiety and depressive disorders, we use a combination of statistical and machine learning techniques to investigate associations with the generalized anxiety disorder assessment (GAD-7) scores in a UK Biobank sample of 90,882 individuals. As in previous studies, we observed that females exhibited higher GAD-7 scores than males regardless of genotype. Interestingly, we found no significant effects on anxiety from individual circadian gene variants; only circadian genotypes with multiple SNP variants showed significant associations with anxiety. For both sexes, severe anxiety is associated with a 120-fold increase in odds for individuals with CRY2_AG(rs1083852)/ZBTB20_TT(rs1394593) genotypes and is associated with a near 40-fold reduction in odds for individuals with PER3-A_CG(rs228697)/ZBTB20_TT(rs1394593) genotypes. We also report several sex-specific associations with anxiety. In females, the CRY2/ZBTB20 genotype combination showed a > 200-fold increase in odds of anxiety and PER3/ZBTB20 and CRY1 /PER3-A genotype combinations also appeared as female risk factors. In males, CRY1/PER3-A and PER3-B/ZBTB20 genotype combinations were associated with anxiety risk. Mediation analysis revealed direct associations of CRY2/ZBTB20 variant genotypes with moderate anxiety in females and CRY1/PER3-A variant genotypes with severe anxiety in males. The association of CRY1/PER3-A variant genotypes with severe anxiety in females was partially mediated by extreme evening chronotype. Our results reinforce existing findings that females exhibit stronger anxiety outcomes than males, and provide evidence for circadian gene associations with anxiety, particularly in females. Our analyses only identified significant associations using two-gene combinations, underscoring the importance of combined gene effects on anxiety risk. We describe novel, robust associations between gene combinations involving the ZBTB20 SNP (rs1394593) and risk of anxiety symptoms in a large population sample. Our findings also support previous findings that the ZBTB20 SNP is an important factor in mood disorders, including seasonal affective disorder. Our results suggest that reduced expression of this gene significantly modulates the risk of anxiety symptoms through direct influences on mood-related pathways. Together, these observations provide novel links between the circadian clockwork and anxiety symptoms and identify potential molecular pathways through which clock genes may influence anxiety risk.

www.nature.com/scientificreports/chronotype (ID: 1180), household income (ID: 738), substance addiction (ID: 20457), and Townsend Material Deprivation score (ID: 22189).Substance addiction was defined by self-report answers to a survey question asking participants if they had an ongoing addiction or dependence on illicit or recreational drugs, and they were given the answer options "No", "Yes", and "Prefer not to answer".Chronotype was defined by self-report responses where individuals classified themselves as definite morning or evening-types, partial morning or evening-types, or had the option of selecting "I don't know" or "prefer not to answer".Household income was also collected through self-report where individuals were asked to report their average household income by selecting one of five pre-specified ranges, also with "I don't know" or "Prefer not to answer" options.Individuals who selected "I don't know" or "Prefer not to answer" for any of the covariates of interest were removed from the analysis.Townsend Material Deprivation score was calculated by participant postal code.
Anxiety was assessed using the Generalized Anxiety Disorder 7-item scale 51 .Additional measures related to anxiety are available from the UK Biobank database, including the ICD-10 (International Classification of Disease for anxiety disorder), clinical diagnoses of anxiety, the use of anxiolytic medicine, and self-reported measures of anxiety-related doctor visits and symptoms of anxiety.Although these variables, particularly the clinical diagnoses of and treatments for anxiety, are excellent measures of anxiety disorders, using these measures significantly reduces the sample size for statistical analysis of gene variant associations.Thus, we chose to include the well-supported GAD-7 instrument as the anxiety measure to maximize the sample size for our analyses.

Feature engineering and selection
To account for the effects of population structure and batch-based genotyping, UK Biobank researchers utilized several stages of quality control [ 52,53 ].First, several different SNP-based metrics were used identify and eliminate less reliable genotyping results.If SNPs were missing in multiple batches, then they were removed from analysis [ 53 ].SNPs with a minor allele frequency less than 1 percent (MAF < 1%) were removed from analysis [ 53 ].Next, researchers focused exclusively on high-quality SNPs to identify poor-quality samples [ 53 ].Finally, principal components analysis and relatedness inference were used for sample-based inference.From these quality control steps, UK Biobank researchers identified few SNPs and samples to be removed [ 53 ].These researchers performed whole-genome imputation with IMPUTE2 using a diverse reference panel.Imputation information scores were used to assess imputation, and these scores revealed effective imputation for SNPs of varying MAFs [ 52 ].In our analysis, any individuals lacking relevant data pertaining to any of our selected features or outcome were Table 1.Summary table depicting the associations of clock genes analyzed in this study with anxiety, depression, and sleep/wake patterns.A table was created for the genes analyzed in this study, summarizing their previous associations with anxiety, depression, and chronotype, and the methods that each study used.

SAD GWAS ZBTB20
Hua et al. 85 Candidate gene, case-control CRY1 Kim et al. 24 Candidate genes, statistical testing CLOCK Lavebratt et al. 5 Candidate gene approach PER2 Lavebratt et al. 6 CRY2 expression and statistical analysis CRY2 www.nature.com/scientificreports/removed from the population analyzed; no imputation was performed for these missing values.This left 90,882 UK Biobank participants for analysis.One-hot encoding was performed to transform categorical variables into numeric values that can be read for machine learning analysis.For categorical variables with n categories, n-1 new columns were created.One of the categories was considered the reference category and excluded, since it could be inferred from the other columns.With SNP data, for example, two new columns were created for the two less frequent genotypes, and the common genotype column was considered as the reference category (Supplementary Fig. 2).SNP data was also imputed for CLOCK, PER2, PER3-A, and PER3-C, consistent with the imputation that UK Biobank researchers had already performed on CRY1, CRY2, PER3-B, and ZBTB20 using which were imputed by UK Biobank researchers using the Haplotype Reference Consortium 54 , and UK10K and 1000 Genomes reference panels 55,56 .These 6 clinical features and 8 single-genotype features were examined for associations with anxiety.Also, we investigated gene combinations involving two genotypes and their respective variants to examine pairwise interactions.This generated an additional 8C2*8 two-way features; of eight total genes, any two can exist in a pair and there are eight potential genotypic combinations that paired genes may have, resulting in 224 total genotype combinations.
The GAD-7 is a self-report scale that ranges from 0 to 21. GAD-7 scores have commonly been broken into thresholds where scores ≤ 4 indicate minimal anxiety, 5-9 suggest mild anxiety, 10-14 suggest moderate anxiety, and ≥ 15 indicate severe anxiety.Since a score of 8 is a commonly held cutoff for symptoms of mild anxiety 57,58 , we used this supported cutoff to establish anxiety presence.From here, we adhered to the cutoffs for anxiety severity and used the following thresholds: mild anxiety (8-11), moderate anxiety (12-15), and severe anxiety (≥ 16).Individuals with a GAD-7 score < 8 were used as controls for the data analysis.We split these categories into three different binary outcomes for multiple multivariate logistic regression analyses.Our data was reprocessed four times into four total separate datasets where the data either was or was not one-hot encoded and the outcome variable was either binary or continuous, so that multivariate linear regression, multivariate logistic regression, the Sheirer Ray Hare Test, and mediation analysis could be performed with the correct types of data.
We used a combination of feature selection methods to determine which features to use in our subsequent analyses.We used a combination of multiple ranking-based and subset-based feature selection methods to mitigate the inherent bias of individual feature selection methods, choosing features that were ranked highly by all feature selection algorithms.Chi-square, InfoGain (IG), and ReliefF (ReF) are ranking-based feature selection methods that rank features by their contribution to the disorder outcome.The chi-square method calculates the association between features and anxiety outcomes using the chi-squared score 59 .InfoGain (IG) ranks features based on the amount of entropy each feature explains 60,61 .ReliefF (ReF) scores features based on their value, relative to their nearest-neighbor instance 62 .Joint mutual information (JMI) and minimum redundancy maximum relevance (MRMR) are subset-based feature selection methods.Both of these methods determine subsets in the feature space, and select feature subsets that have the strongest relationships with an outcome and the weakest relationship with other features by evaluating and comparing these two different interactions.Joint mutual information (JMI) selects features for a subset that maximize the cumulative sum of joint mutual information when added to the subset 63 .Minimum redundancy maximum relevance (MRMR) iteratively selects a subset of features that have the most correlation with the class, and the least correlation with other features 64 .Bootstrapping was performed for each feature selection method by running it 50 times and taking features that appeared in the top 50 features at least 70 percent of the time.Then, the results across these five methods were compared using a sum of ranks and the 25 features that performed the best over the five techniques were used in our statistical analysis.
Since there was still high dimensionality in the dataset following feature selection and we observed initial overfitting of our model, we used Variance Inflation Factor (VIF) and Akaike Information Criterion (AIC) as we performed our regressions to identify important features.VIF was used to identify multicollinearity and variables with a VIF score > 10 were excluded from the analysis 67 .We also used AIC, which is a model selection algorithm that uses sequential replacement to identify features with low multicollinearity and strong association with GAD-7 68 .Features that were deemed important by both selection methods, in addition to machine learning feature selection methods described above, were used in subsequent multivariate analyses.
Multivariate linear and logistic regression analyses were performed using the Statsmodels library in R 69 .We performed multivariate linear regression to predict continuous GAD-7 scores with genotypic and clinical independent variables.First, a Durbin-Watson statistic was obtained to check the independence of residuals assumption 70 , and then a scatterplot was constructed to confirm a linear relationship between each independent feature and the collective of independent features with GAD-7 scores.Studentized residuals were plotted against the unstandardized predicted values to check that the assumption of homoscedasticity was met.No outliers, high leverage points, or highly influential points were detected during analysis, and the normality of residuals was confirmed via a histogram with a superimposed normal curve and a P-P Plot.After performing multivariate linear regression, P-value corrections were performed using the Benjamini Hochberg (BH) correction, due to unplanned pairwise comparisons between features 71 .Next, multivariate logistic regression was performed to assess genotypic and clinical predictors for mild, moderate, and severe anxiety outcomes.A linear relationship was confirmed between continuous independent variables and the logit-transformed GAD-7 outcome for all three anxiety classifications, and no outliers, high leverage points, or highly influential points were detected during analysis.Following the analysis, p-values were also adjusted for multivariate logistic regression using the Benjamini Hochberg (BH) procedure 71 .
We sought to analyze the two-way interactions (genotype and sex) of the SNP combinations that appeared in our multivariate analysis using two-way ANOVA.However, the Shapiro-Wilk test of normality showed that our data was not normally distributed 72 .Therefore, to identify sex-specific differences in average GAD-7 scores for two-way gene combinations, we performed the Scheirer Ray Hare Test in R 73 , which instead compares the median GAD-7 scores across groups.Bar plots of these significant combinations revealed that GAD-7 score distributions were similar enough in shape across groups to have their medians compared.
Mediation analysis was performed to examine whether features were directly associated with anxiety or indirectly associated with anxiety through extreme morning or extreme evening chronotype.Our mediation analysis was conducted in the Mediation library in R 74 , and this analysis was completed for SNP combinations that were significant in the multivariate logistic regression analysis and their associated anxiety outcome(s).This analysis was performed two times for each combination: once with extreme morning type as the mediator and once with extreme evening type as the mediator.Clinical variables including addiction, age, income, and Townsend deprivation index scores were used as confounders when determining the mediation effect of chronotype.

Decision trees summarize genotypic associations with anxiety symptoms
For both sexes, ZBTB20 variants occurred in a risk combinations with CRY2_AG and protective combinations with PER3-A_CG (Fig. 4).We constructed association networks to provide a visual summary of our analysis.For females, the CRY1_GG/PER3-A_GG combination was associated with severe anxiety both directly and indirectly through extreme evening type (Fig. 5).ZBTB20 was associated with increased anxiety risk in combinations with CRY2_AG and CRY2_GG and was associated with decreased anxiety risk in combinations with PER3-A_CG and PER3-A_CC (Fig. 4).In males, the CRY1_CC/PER3-A_GG combination was directly associated with severe anxiety, and ZBTB20 variants occurred in risk combinations with CRY2_AG and PER3-B_GG (Fig. 6).

Discussion
There is a growing body of evidence supporting the roles of clock gene variants and circadian disruption in anxiety.However, many of these studies have utilized GWAS and PheWAS approaches making it difficult to detect synergistic effects between genotypes or to explore whether significant genotypes are directly or indirectly associated with anxiety.Novel machine learning approaches have shown promise in illuminating these synergistic effects and proposing potential mechanisms of clock pathways that may influence anxiety 14 and sleep disturbance 38 .Utilizing similar machine learning approaches to analyze clock gene associations with anxiety in  www.nature.com/scientificreports/ a large UK Biobank dataset, we report three main findings: (1) Clock genotype combinations including ZBTB20 variants exhibit combination-specific effects on anxiety, (2) Clock variant combinations associated with anxiety tend to display sex-specific effects, and (3) Circadian-related variants linked to anxiety risk have both direct (chronotype-independent) influences and indirect (chronotype-mediated) influences on anxiety symptoms.

Genotype combinations with ZBTB20 exhibit diverse associations with anxiety
In this study, we did not observe any single-gene associations with anxiety; only genotype combinations were identified as significant predictors for GAD-7 outcomes.ZBTB20_TT was present in nearly every risk combination-with CRY2 and PER3-B and protective combination-with CLOCK and PER3-A.These findings reinforce results from previous GWAS and target gene studies and suggest that ZBTB20_TT may have an important  regulatory effect on clock genes involved in anxiety.ZBTB20 is a zinc finger transcriptional repressor protein that is abundant in the hippocampus and is known to have an important role in hippocampal development 75,76 .Previous research from Ho et al. 46 revealed that the minor T-allele of ZBTB20 was associated with lower ZBTB20 mRNA expression and an increased risk for seasonal affective disorder (SAD) 46 .These authors also found that 32 genes associated with SAD were enriched when ZBTB20 levels were reduced 46 suggesting that ZBTB20 plays an important role in the regulation of clock gene expression.Indeed, other studies have found that ZBTB20 loss is associated with impaired circadian rhythms 77 , and that epigenetic changes inhibiting ZBTB20 expression are associated with MDD 47 .Our findings provide further support for ZBTB20 as a regulator for circadian clock genes and demonstrate that genotype combinations that include ZBTB20 variants can exhibit sex-specific outcomes on anxiety symptoms.In previous studies, the A-allele of CRY2 has been associated with chronicity patterns characteristic of depressive symptoms 78 , and CRY2_AG has appeared in a risk combination for anxiety 14 .Decreases in CRY2 mRNA have previously been observed in depressed bipolar patients 6 , suggesting that ZBTB20 could act as a repressor of another gene that represses CRY2 transcription.Therefore, reductions in ZBTB20 could indirectly contribute to lower CRY2 expression.This trend was also observed in females who exhibited protective PER3-A combinations with ZBTB20_ TT (PER3-A_CC/ZBTB20_TT and PER3-A_CG/ZBTB20_TT).The PER3-A G-allele and GG genotype have previously been associated with MDD 25,79 , anxiety 13 , and eveningness 25,80 .Previous mathematical modeling insights suggest that greater PER3 stability contributes to slight increases in period and large reductions in Figure 4. Decision tree summarizes multivariate and mediation analysis findings for genotypes associated with anxiety in both sexes.A decision tree was constructed to visualize associations between clock genes and anxiety that appeared in the overall dataset.Red and blue ovals represent genotypes belonging to risk and protective combinations, respectively, while gray ovals represent genotypes belonging to both risk and protective combinations.Dashed lines represent associations with anxiety, supported by multivariate regression, while solid lines represent effects found by mediation analysis.
Figure 5. Decision tree summarizes multivariate and mediation analysis findings for genotypes associated with anxiety in females.A decision tree was constructed to visualize associations between clock genes and anxiety that appeared in females.Red and blue ovals represent genotypes belonging to risk and protective combinations, respectively, while gray ovals represent genotypes belonging to both risk and protective combinations.Dashed lines represent associations with anxiety, supported by multivariate regression, while solid lines represent effects found by mediation analysis (*Exhibited partial mediation by extreme evening type).clock amplitude, contributing to circadian misalignment 25 .Thus, ZBTB20 could serve as a repressor for the transcription of PER3.
In males, PER3-B_GG/ZBTB20_TT was associated with increased odds of mild anxiety.However, the A-allele of PER3-B has previously been the allele associated with increased odds of MDD and anxiety 14,79 , suggesting a potential combination-specific effect.Indeed, increases in PER3-B expression have been shown to associate with circadian disruption 19,81 .Loss of ZBTB20 repression activity may lead to increased PER3-B expression and greater circadian disruption, influencing the likelihood of anxiety symptoms.
Finally, we observed that the CLOCK_AA/ZBTB20_TT and PER2_AG/ZBTB20_TT genotype combinations were protective factors for both sexes.The C-allele of CLOCK has been associated with MDD 79 , seasonal depression 24 , and evening chronotype 82 , supporting a protective association for CLOCK_AA/ZBTB20_TT in both sexes.The G-allele of PER2 has previously been associated with depression vulnerability 5 , so this protective effect may function through alterations in PER2 expression.As the transcription of PER2 is finely tuned in response to environmental light 83 and increased PER2 stabilization leads to circadian disruption in mice 84 , ZBTB20 may act to inhibit a repressor of the PER2 gene.

Genotype combinations exhibit sex-specific associations with anxiety
Our main findings in the regression analyses provide support for sex-specific associations of circadian genotypes with anxiety.CRY1_GG/PER3-A_GG, CRY2_GG/ZBTB20_TT, and CRY2_AG/ZBTB20_TT were risk factors that showed stronger associations in females.Also, PER3-A_CG/ZBTB20_TT and PER3-A_CC/ZBTB20_TT were protective for mild and moderate anxiety in females.In males, CRY1_CC/PER3-A_GG was a risk factor for severe anxiety and PER3-B_GG/ZBTB20_TT was a risk factor for mild anxiety.Sex-specific associations with circadian genes have previously been observed for anxiety 14 and major depressive disorder (MDD) 79 .
Sex-specific associations of CRY2/ZBTB20 and PER3-A/ZBTB20 with anxiety in females suggest that these combinations are involved in sex-specific pathways.We observed that CRY2 combinations (CRY2_GG/ZBTB20_ TT and CRY2_AG/ZBTB20_TT) showed significantly stronger associations with anxiety in females than in males.Because CRY2_AG/ZBTB20_TT also appeared as a risk factor in males, this combination may exert effects through a shared pathway in both sexes.The sex-specific association of PER3-A in females is supported by a previous association with MDD 79 .We also observed that CRY1_GG/PER3-A_GG was associated with severe anxiety in females.Since the C-allele of CRY1 is the risk allele associated with depression 25,27,85 , and the G-allele of PER3-A is the risk allele associated with MDD 25,79 , anxiety 13 , and eveningness 25,80 , these findings provide further support for the sex-specific involvement of PER3-A in female anxiety risk.
In males, CRY1_CC/PER3-A_GG was a risk factor for severe anxiety.Since this combination includes the risk alleles for both genes, this combination could indicate that both genotypes affect anxiety symptoms independently, or in a combination-specific manner.Also, PER3-B_GG/ZBTB20_TT appeared as a risk factor for mild anxiety in males and this genotype combination does not appear to be a risk factor for females.
There are multiple pathways by which clock gene variants may exert sex-specific effects.Glucocorticoid regulation may be a potential sex-dependent pathway by which these genotypes modulate one's odds of anxiety 86 .The glucocorticoid pathway has previously been implicated in mood disorders 87 and clock gene pathways modulate the release of and sensitivity to glucocorticoids 88 .In addition, PER3-A and PER3-B have been identified in several associations with the sleep-wake cycle and diurnal preference 13,38,80 , which are hypothesized to alter mood through the regulation of serotonin [89][90][91] .As the function of the 5-HT serotonin system is intertwined with the circadian system 92,93 , and this system affects mood 94 , serotonin regulation has been implicated as a pathway by which circadian disruption can lead to effects on mood 82,89,95,96 .Furthermore, anxiety symptoms have been shown to associate closely with shifts in serotonin activity 97,98 , and sex differences have been observed in serotonergic transmission 99,100 .Therefore, circadian disruptions due to PER3-A and PER3-B variants could affect Figure 6.Decision tree summarizes multivariate and mediation analysis findings for genotypes associated with anxiety in males.A decision tree was constructed to visualize associations between clock genes and anxiety that appeared in males.Red ovals represent genotypes belonging to risk combinations.Dashed lines represent associations with anxiety, supported by multivariate regression, while solid lines represent effects found by mediation analysis.mood in a sex-specific manner through alterations in 5-HT signaling.These suggested pathways are supported by previous GWAS studies on anxiety, which have identified other genes known to function in neurotransmitter signaling pathways 40,41 .However, previous GWAS on anxiety have not yet identified associations of clock genes with anxiety.

Effects on anxiety may be direct or mediated through chronotype
We observed that CRY2_AG/ZBTB20_TT was directly associated with moderate anxiety in females, suggesting this genotypic combination exerts direct effects on mood in females.Previously, Zafar et al. 14 also found that CRY2_AG was directly associated with anxiety symptoms 14 .The results of the current study suggest that decreases in the transcriptional repression of CRY2 by ZBTB20 may lead to greater transcription of CRY2, which exerts direct effects on anxiety through mood-related pathways.
For both sexes, our multivariate analyses revealed that extreme evening type behavior was associated with an increased risk of anxiety, while extreme morning type was protective against anxiety.Our findings are supported by large-scale GWAS studies that identified clock genes involved in the core feedback loop to be associated with alterations in sleep/wake timing [ 35,37 ].Silva and colleagues (2020) suggested that genotypic variants associated with shifts in chronotype may indirectly affect one's odds of anxiety through the development of symptoms characteristic of various mood disorders 101 .Interestingly, we found that the association between CRY1_GG/PER3-A_GG and severe anxiety in females was partially mediated by extreme evening type behavior.CRY genes activate the circadian loop and function in the retina as light-independent inhibitors of CLOCK/ BMAL heterodimers 102,103 , suggesting their role in circadian rhythm maintenance.As stated above, this PER3-A variant (rs228697) has previously been associated with evening type behavior 13,25 , and a significantly higher risk of anxiety 13 .Altogether, these findings suggest that the modulation of CRY1 expression may lead to alterations in sex-specific mood pathways and diurnal preference pathways in ways that are conducive to anxiety in females.Interestingly, the anxiety risk associated with the co-occurrence of CRY1_GG with PER3-A_GG in females is similar in magnitude to the male-specific association for CRY1_CC/ PER3-A_GG genotypes, suggesting that CRY1 homozygotes, in the presence of PER3-A_GG, may affect anxiety via distinct sex-specific mechanisms.

Limitations
Previous studies have suggested that the UK Biobank population may have a "healthy volunteer" selection bias because only 5% of recruited individuals responded to the invitation.Thus, the Biobank cohort study may not be representative of the UK population 104,105 .For example, UK Biobank study participants were less likely to be socioeconomically deprived, obese, smoke, drink alcohol on a daily basis, and have self-reported health conditions 106 .To counteract the healthy volunteer bias present in the UK Biobank, we controlled for issues of economic status and sex to maximize the generalizability of our results.The UK population and, accordingly, the UK Biobank participants, are predominantly of European Caucasian descent with nearly 95% of the database identifying as 'White' .Although we did not exclude by ethnicity, our results may not be generalizable to populations of non-Caucasian descent given the small representation of ethnic minorities in the analyses.In addition, the UK Biobank offers additional measures of anxiety, including clinical diagnoses, that could be used to test for associations of clock genes with anxiety.However, these measures offered smaller sample sizes relative to the GAD-7 instrument.Thus, we chose to utilize the well-supported GAD-7 instrument to maximize the power of our analyses.Finally, to minimize computational requirements, we selected circadian gene variants that had been linked to chronotype and/or mood disorders in previous studies and did not study all possible circadian-related variants.Therefore, this study may be missing important circadian features that influence anxiety.

Conclusions
In this study, we report sex-dependent, combination-specific, and indirect and direct effects of circadian genotypes on anxiety.ZBTB20 was a feature in several risk and protective genotypic combinations, occurring with CRY2 and PER3-B in risk combinations, and with CLOCK and PER3-A in protective combinations.Several additional clock-related genes were involved in sex-specific associations with anxiety and these polymorphisms likely influence pathways involved in glucocorticoid and serotonin regulation.Together, these observations reinforce previous GWAS insights into the associations of ZBTB20 with mood pathways and suggest that ZBTB20 may have a critical regulatory role, both as a repressor and indirect activator, in the transcription of clock genes.In females, we found that the CRY2_AG/ZBTB20_TT genotype, our strongest predictor of anxiety, was directly associated with anxiety.The CRY1_GG/PER3-A_GG genotype in females exhibited effects partially mediated by extreme evening-type behavior, suggesting that circadian effects on anxiety can be both direct and/or mediated by chronotype.

Figure 2 .
Figure2.Mediation analysis reveals a combination directly associated with anxiety and a combination whose association is partially mediated by chronotype in females.Mediation analysis was performed in females for mild, moderate, and severe anxiety classifications with extreme morning type and extreme evening type entered as potential mediators.The black line from genotypic combination to outcome indicates a direct effect.Red dotted lines indicate a mediation effect through extreme evening type, and black dotted lines that are crossed indicate no mediation effect.(a) A direct effect was observed between CRY1_GG/PER3-A_GG and severe anxiety with extreme morning type as the mediator (p = 0.02), and a partial mediation effect was observed with extreme evening type as the mediator (direct p = 0.04; indirect p = 2E-16).(b) A direct effect between CRY2_AG/ ZBTB20_TT with moderate anxiety was observed (direct morning p = 0.02; direct evening p = 0.04).

Figure 3 .
Figure 3. Mediation analysis reveals CRY1_CC/PER3-A_GG is directly associated with severe anxiety in males.Mediation analysis was performed in males for mild, moderate, and severe anxiety classifications with extreme morning type and extreme evening type entered as potential mediators.The black line from genotypic combination to outcome indicates a direct effect.Red dotted lines indicate a mediation effect through extreme evening type, and black dotted lines that are crossed indicate no mediation effect.

Table 2 .
Multivariate linear and logistic regression reveal significant risk and protective factors for anxiety in both sexes.Multivariate linear and logistic regression were performed for genotypic and clinical features in association with anxiety.Values under the estimate column from multivariate linear regression provide constant estimates, where values > 0 indicate a risk effect and values < 0 indicate a protective effect.Values in mild, moderate, and severe columns indicate odds ratios provided by multivariate logistic regression, where values > 1 indicate a risk effect and values < 1 indicate a protective effect (*p < 0.01, **p < 0.001, ***p < 0.0001).

Table 3 .
Multivariate linear and logistic regression reveal risk and protective factors for anxiety in females.
Multivariate linear and logistic regression were performed for genotypic and clinical features in association with anxiety.Values under the estimate column from multivariate linear regression provide constant estimates, where values > 0 indicate a risk effect and values < 0 indicate a protective effect.Values in mild, moderate, and severe columns indicate odds ratios provided by multivariate logistic regression, where values > 1 indicate a risk effect and values < 1 indicate a protective effect (*p < 0.01, **p < 0.001, ***p < 0.0001).

Table 4 .
Multivariate linear and logistic regression reveal significant risk and protective factors for anxiety in males.Multivariate linear and logistic regression were performed for genotypic and clinical features in association with anxiety.Values under the estimate column from multivariate linear regression provide constant estimates, where values > 0 indicate a risk effect and values < 0 indicate a protective effect.Values in mild, moderate, and severe columns indicate odds ratios provided by multivariate logistic regression, where values > 1 indicate a risk effect and values < 1 indicate a protective effect (*p < 0.01, **p < 0.001, ***p < 0.0001).Vol:.(1234567890)Scientific Reports | (2023) 13:22304 | https://doi.org/10.1038/s41598-023-49644-7